Property taxes are an important revenue raising tool for municipalities such as cities and towns. Tax assessment is a method of allocating property taxes between individuals owning real estate in the municipality. Tax allocation using the assessment method generally works according to the following process. First, each parcel of real estate within the municipality is “assessed”—that is, assigned a relative value for the limited purpose of collecting a property tax. Next, the total assessed value for the municipality is calculated by summing the assessed values for all parcels. Finally, the property tax to be levied on each parcel is allocated by multiplying each respective parcel's fraction of the total assessed value by the total property tax to be raised. As may be appreciated from this description, an assessed property value need have no relation with the market value of the property, so long as all assessed values are appropriately determined relative to each other. An assessed value therefore represents the relative value of a parcel of real estate with respect to its neighbors.
Fairness in allocating the tax burden requires that parcels be reassessed on a regular basis to capture increases and decreases of their relative values. However, determining which parcels have been improved and the extent of the improvements may be a difficult, expensive, and time-consuming job, especially if it must be undertaken by individual tax assessors physically viewing every parcel in the municipality. One tool that can simplify the process is remote sensing. Remote sensing, in this context, means obtaining aerial or satellite imagery of a municipality at multiple times. A tax assessor may determine which properties have been improved over a relevant assessment period by comparing a “before” image captured at a time when a prior tax assessment was made, with an “after” image captured more recently for which a reassessment must be made. While side-by-side visual image comparison may be a valuable tool, nevertheless it can be mentally taxing, as many parcels look nearly identical, especially those in a development or sub-development that are laid out to provide a uniform appearance. Therefore, automated image analysis may be employed.
Image analysis is the process of extracting meaningful information from images. One type of image analysis particularly useful in this context is geographic object-based image analysis (GEOBIA). Digital images are formed from pixels; GEOBIA takes those pixels and performs two main processes, segmentation and classification. Image segmentation is the process of partitioning a digital image into segments, each of which is formed from multiple pixels having a common characteristic such as color or brightness. Such segments therefore represent a type of land cover; that is, grass, asphalt, trees, dirt, water, and so on. Statistics may then be applied to the segments to classify each segment by the type of land cover it represents.
However, image analysis is required to extract information that is meaningful, which in the remote sensing context includes changes to parcels that affect the assessed value, such as the addition of a rooftop solar photovoltaic system or an in-ground swimming pool. Meaningful information does not include changes between the images that are caused by transient phenomena, such as shadows, differences in lighting generally, the presence or absence of people, automobiles, other movable objects, and so on. Distinguishing between these types of changes is, in general, a very hard problem, and existing segmentation and classification software suffers from a variety of problems distinguishing meaningful segments from meaningless segments, especially in the context of performing a tax assessment.